Experimental characterization and numerical simulation of the humidity absorption process in glass reinforced composites under dissymmetric exposure conditions
Bibliographic record
Abstract
Abstract This article investigates the dissymmetrical water absorption process in E‐glass fiber reinforced polyester composites containing fillers and low profile additives. Two different cases of dissymmetrical exposures to water were considered. In the first case, only one side of the aged specimen was exposed to water at different temperatures, the other side was exposed to air. In the second case, both sides were exposed to water, but at different temperatures on each side. The moisture diffusivity and the maximum moisture content reached by the composites were determined using the gravimetric test method. In the first case of the dissymmetrical immersion, the temperature inside the aged specimen was found to be almost constant and the kinetic of the water diffusion was found to obey perfectly the one‐dimensional Fick's second law. In the second case, the specimen temperature varies from one side to the other, thus preventing the use of Fick's laws. Alternatively, the use of the finite element code ABAQUS provided an excellent agreement between the experimental and simulated results in both cases of dissymmetrical immersions, as well as in the case of symmetrical immersion where the two sides were exposed to the same environmental conditions. POLYM. COMPOS., 2009. © 2009 Society of Plastics Engineers
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".